Moneerh Aleedy¹, Riham AlSmariy², Wejdan Alsurayyi¹* and Suad Almutairi¹

¹Information Technology Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
²Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

*Corresponding Author: Wejdan Alsurayyi, Information Technology Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Received: February 07, 2024; Published: February 21, 2024

Abstract

The gender recognition system by a sample of voice has an excellent mechanism based on many factors and features of sound signals like frequency, pitch, and loudness. This paper implements a machine to distinguish between the male's and female's voice. The experiments are implemented by two techniques to split speech dataset: train_test_split function and 10-fold Cross-Validation on the training set (after splitting the data into train and test set), to get the highest result and improve the model and make a comparison between them. Then, the K-best feature selection is used as a filtering method and Recursive Feature Elimination (RFE) as a wrapper method. This paper investigates various machine learning algorithms, such as logistic regression, random forest, SVM, gradient boosting, and decision tree. The performance was evaluated using different metrics: accuracy, recall, precision, F-score, and confusion-matrix. The results showed that splitting the dataset using 10-fold CV with RFE provides the best result for all ML algorithms and the best model is the random forest, it gives the highest percentage in all evaluation methods.

Keywords: Machine Learning; Voice; RFE; k-Best; Random Forest

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Citation

Citation: Wejdan Alsurayyi., et al. “Gender Recognition by Voice Using Machine Learning".Acta Scientific Computer Sciences 6.3 (2024): 07-12.

Copyright

Copyright: © 2024 Wejdan Alsurayyi., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.